•Cognitive function is important for driving performance, irrespective of age.•Most essential are executive function, complex attention, and dual tasking ability.•Older persons drive slower and more ...variable than younger aged persons.•Older driver impairment could be the result of age-related changes in cognition.•Limited aging research has focused on evaluating brain dynamics of driving.
Older drivers are at a severely higher risk for motor vehicle crash involvement. Due to the global aging of the population, this increased crash risk has a significant impact on society, as well as on an older individual’s quality of life. For this reason, there is a need for understanding how normal age-related changes in cognition and underlying brain dynamics impact driving performance to identify the functional and neurophysiological biomarkers that could be used to design strategies to preserve or improve safe driving behavior in older persons. This review provides an overview of the literature on age-related changes in cognitive functioning and brain dynamics that impact driving simulator performance of healthy persons. A systematic literature search spanning the last ten years was conducted, resulting in 22 eligible studies. Results indicated that various aspects of cognition, most importantly executive function, complex attention, and dual tasking, were associated with driving performance, irrespective of age. However, there was a distinct age-related decline in cognitive and driving performance. Older persons had a more variable, less consistent driving simulator performance, such as more variable speed adaptation or less consistent lane keeping behavior. Only a limited number of studies evaluated the underlying brain dynamics in driving performance. Therefore, future studies should focus on implementing neuroimaging techniques to further unravel the neural correlates of driving performance.
Community participation and the formation of social networks are crucial for a qualitative life. To this end, transportation plays an essential role. Many autistic people rely on public ...transportation for their mobility needs. However, research shows that it is not always easy for them to use it. The issues they face when using public bus transport have not yet been thoroughly studied. The current case study in Flanders aimed to give autistic people the opportunity to express the issues they face while using public bus transportation. A qualitative hermeneutic phenomenological study was carried out. Semistructured interviews were conducted with 17 autistic individuals. The interviews were analyzed based on the interpretative phenomenological analysis method. Three main themes emerged: creating predictability, limiting stimuli, and open and accessible communication. In addition, various coping strategies were described, such as the use of noise-canceling headphones. The results of this study may lead to a more autism-friendly public transportation environment.
Lay Abstract
Transportation plays an essential role in daily life, allowing people to participate in the community and form social relationships. Many autistic people rely on public transportation to meet their mobility needs. However, research shows that it is not always easy for them to use it. The exact issues autistic individuals face when traveling with public transportation and how public transportation can be made more autism-friendly have yet to be researched. The current study allowed autistic individuals to express themselves regarding issues they face while traveling by public bus transportation, to raise awareness for making public transportation more autism-friendly. We interviewed 17 autistic individuals about their experiences riding the bus. Three main themes emerged from the results: creating predictability, limiting stimuli, and open and accessible communication. If transport companies take initiatives related to these themes, autistic people traveling by bus can have a more pleasant experience. Participants also described coping strategies for stressful or uncomfortable situations while using public bus transportation, such as using noise-cancelling headphones or digital applications for real-time route tracking, etc. These findings may lead to a more autism-friendly public transportation.
In previous research, significant effects of weather conditions on car crashes have been found. However, most studies use monthly or yearly data and only few studies are available analyzing the ...impact of weather conditions on daily car crash counts. Furthermore, the studies that are available on a daily level do not explicitly model the data in a time-series context, hereby ignoring the temporal serial correlation that may be present in the data. In this paper, we introduce an integer autoregressive model for modelling count data with time interdependencies. The model is applied to daily car crash data, metereological data and traffic exposure data from the Netherlands aiming at examining the risk impact of weather conditions on the observed counts. The results show that several assumptions related to the effect of weather conditions on crash counts are found to be significant in the data and that if serial temporal correlation is not accounted for in the model, this may produce biased results.
With age, a decline in attention capacity may occur and this may impact driving performance especially while distracted. Although the effect of distraction on driving performance of older drivers has ...been investigated, the moderating effect of attention capacity on driving performance during distraction has not been investigated yet. Therefore, the aim was to investigate whether attention capacity has a moderating effect on older drivers' driving performance during visual distraction (experiment 1) and cognitive distraction (experiment 2). In a fixed-based driving simulator, older drivers completed a driving task without and with visual distraction (experiment 1, N=17, mean age 78 years) or cognitive distraction (experiment 2, N=35, mean age 76 years). Several specific driving measures of varying complexity (i.e., speed, lane keeping, following distance, braking behavior, and crashes) were investigated. In addition to these objective driving measures, subjective measures of workload and driving performance were also included. In experiment 1, crash occurrence increased with visual distraction and was negatively related to attention capacity. In experiment 2, complete stops at stop signs decreased, initiation of braking at pedestrian crossings was later, and crash occurrence increased with cognitive distraction. Interestingly, for a measure of lane keeping (i.e., standard deviation of lateral lane position (SDLP)), effects of both types of distraction were moderated by attention capacity. Despite the decrease of driving performance with distraction, participants estimated their driving performance during distraction as good. These results imply that attention capacity is important for driving. Driver assessment and training programs might therefore focus on attention capacity. Nonetheless, it is crucial to eliminate driver distraction as much as possible given the deterioration of performance on several driving measures in those with low and high attention capacity.
The i-DREAMS project established a 'Safety Tolerance Zone (STZ)' to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of the human ...element in driving behavior. This paper aims to model the inter-relationship among driving task complexity, operator and vehicle coping capacity, and crash risk. Towards that aim, data from 80 drivers, who participated in a naturalistic driving experiment carried out in three countries (i.e., Belgium, Germany, and Portugal), resulting in a dataset of approximately 19,000 trips were collected and analyzed. The exploratory analysis included the development of Generalized Linear Models (GLMs) and the choice of the most appropriate variables associated with the latent variables "task complexity" and "coping capacity" that are to be estimated from the various indicators. In addition, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modeled. Comparisons on the performance of such models, as well as a discussion on behaviors and driving patterns across different countries and transport modes, were also provided. The findings revealed a positive relationship between task complexity and coping capacity, indicating that as the difficulty of the driving task increased, the driver's coping capacity increased accordingly, (i.e., higher ability to manage and adapt to the challenges posed by more complex tasks). The integrated treatment of task complexity, coping capacity, and risk can improve the behavior and safety of all travelers, through the unobtrusive and seamless monitoring of behavior. Thus, authorities should utilize a data system oriented towards collecting key driving insights on population level to plan mobility and safety interventions, develop incentives for road users, optimize enforcement, and enhance community building for safe traveling.
Introduction: An efficient decision-making process is one of the major necessities of road safety performance analysis for human safety and budget allocation procedure. Method: During the road safety ...analysis procedure, data envelopment analysis (DEA) supports policymakers in differentiating between risky and safe segments of a homogeneous highway. Cross-risk, an extension of the DEA models, provides more information about risky segments for ranking purpose. After identification of risky segments, the next goal is to identify the factors that are major contributors in making that segment risky. Results: This research proposes a methodology to analyze road safety performance by using a combination of DEA with the decision tree (DT) technique. The proposed methodology not only provides a facility to identify problematic road segments with the help of DEA but also identifies contributing factors with the help of DT. Practical applications: The applicability of the proposed model will help policymakers to identify the major factors contributing to road accidents and analysis of safety performance of road infrastructure to allocate the budget during the decision-making process.
•Road safety performance analysis is necessary for human safety and budget allocation procedure.•Applied DEA method for identify risky and safe segments of a homogeneous highway.•Applied DT method to identify impact of four major factors on safety level.•Speed and flow were found as critical factors contributing in increasing safety level of motorways.
This research utilises statistical modelling to explore the impact of roadway infrastructure elements, primarily those related to cross-section design, on crash occurrences in urban areas. ...Cross-section design is an important step in the roadway geometric design process as it influences key operational characteristics like capacity, cost, safety, and overall functionality of the transport system entity. Evaluating the influence of cross-section design on these factors is relatively straightforward, except for its impact on safety, especially in urban areas. The safety aspect has resulted in inconsistent findings in the existing literature, indicating a need for further investigation. Negative binomial (NB) models are typically employed for such investigations, given their ability to account for over-dispersion in crash data. However, the low sample mean and under-dispersion occasionally exhibited by crash data can restrict their applicability. The generalised Poisson (GP) models have been proposed as a potential alternative to NB models. This research applies GP models for developing crash prediction models for urban road segments. Simultaneously, NB models are also developed to enable a comparative assessment between the two modelling frameworks. A six-year dataset encompassing crash counts, traffic volume, and cross-section design data reveals a significant association between crash frequency and infrastructure design variables. Specifically, lane width, number of lanes, road separation, on-street parking, and posted speed limit are significant predictors of crash frequencies. Comparative analysis with NB models shows that GP models outperform in cases of low sample mean crash types and yield similar results for others. Overall, this study provides valuable insights into the relationship between road infrastructure design and crash frequency in urban environments and offers a statistical approach for predicting crash frequency that maintains a balance between interpretability and predictive power, making it more viable for practitioners and road authorities to apply in real-world road safety scenarios.
•This study aims to evaluate the traffic safety effects of a teleworking policy.•An activity-based transportation model is applied to produce exposure variables.•Crash prediction models are developed ...at an aggregate level (i.e. traffic analysis zone).•Prediction models are developed within the geographically weighted generalized linear modeling framework.•The results show traffic safety benefits of conducting the teleworking scenario.
Travel demand management (TDM) consists of a variety of policy measures that affect the effectiveness of transportation systems by changing travel behavior. The primary objective of such TDM strategies is not to improve traffic safety, although their impact on traffic safety should not be neglected. The main purpose of this study is to simulate the traffic safety impact of conducting a teleworking scenario (i.e. 5% of the working population engages in teleworking) in the study area, Flanders, Belgium. Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models should also be developed at an aggregate level. Given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, zonal crash prediction models (ZCPMs) within the geographically weighted generalized linear modeling framework are developed to incorporate the spatial variations in association between the number of crashes (including fatal, severe and slight injury crashes recorded between 2004 and 2007) and other explanatory variables. Different exposure, network and socio-demographic variables of 2200 traffic analysis zones (TAZs) are considered as predictors of crashes. An activity-based transportation model framework is adopted to produce detailed exposure metrics. This enables to conduct a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this study, several ZCPMs with different severity levels and crash types are developed to predict crash counts for both the null and the teleworking scenario. The results show a considerable traffic safety benefit of conducting the teleworking scenario due to its impact on the reduction of total vehicle kilometers traveled (VKT) by 3.15%. Implementing the teleworking scenario is predicted to reduce the annual VKT by 1.43billion and the total number of crashes to decline by 2.6%.
Advanced driver assistance systems (ADASs) have recently gained popularity as they assist vehicle operators in staying within safe boundaries, helping them thereby to prevent possible collisions. ...However, despite their success and development, most ADAS use common and deterministic warning thresholds for all drivers in all driving environments. This may occasionally lead to the issuance of annoying inadequate warnings, due to the possible differences between drivers, the changing environments and driver statuses, thus reducing their acceptance and effectiveness. To fill this gap, this paper proposes adaptive algorithms for commonly used warnings based on real-time traffic environments and driver status including distraction and fatigue. We proposed adaptive algorithms for headway monitoring, illegal overtaking, over-speeding, and fatigue. The algorithms were then tested using a driving simulator. Results showed that the proposed adaptive headway warning algorithm was able to automatically update the headway warning thresholds and that, overall, the proposed algorithms provided the expected warnings. In particular, three or four different warning types were designed to distinguish different risk levels. The designed real-time intervention algorithms can be implemented in ADAS to provide warnings and interventions tailored to the driver status to further ensure driving safety.
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